Memristor models for machine learning

In the quest for alternatives to traditional CMOS, it is being suggested that digital computing efficiency and power can be improved by matching the precision to the application. Many applications do not need the high precision that is being used today. In particular, large gains in area- and power efficiency could be achieved by dedicated analog realizations of approximate computing engines. In this work, we explore the use of memristor networks for analog approximate computation, based on a machine learning framework called reservoir computing. Most experimental investigations on the dynamics of memristors focus on their nonvolatile behavior. Hence, the volatility that is present in the developed technologies is usually unwanted and it is not included in simulation models. In contrast, in reservoir computing, volatility is not only desirable but necessary. Therefore, in this work, we propose two different ways to incorporate it into memristor simulation models. The first is an extension of Strukov's model and the second is an equivalent Wiener model approximation. We analyze and compare the dynamical properties of these models and discuss their implications for the memory and the nonlinear processing capacity of memristor networks. Our results indicate that device variability, increasingly causing problems in traditional computer design, is an asset in the context of reservoir computing. We conclude that, although both models could lead to useful memristor based reservoir computing systems, their computational performance will differ. Therefore, experimental modeling research is required for the development of accurate volatile memristor models.Comment: 4 figures, no tables. Submitted to neural computatio

Synthesis and characterization of bulk and thin film antimony-selenium phase change alloys

Phase change alloys have recently gained increasing attention due to their application in developing phase change random memory (PRAM) devices, as Flash memory based devices are rapidly approaching their technological limitations. The most dominant features of PRAM devices are its non-volatile nature, compatible with present day IC\u27s manufacturing process, high density, fast operation, low power consumption etc; Devices built on binary alloys such as Antimony - Selenium (SbSe) exhibit certain superior properties such as fast operation, reduced power consumption, economical etc. compared to that of ternary alloy (GST). In order to understand this behavior in detail, bulk SbxSe 100-x (40 ≤ x ≤ 70) alloys are synthesized and deposited as thin films on silicon (100) plane substrate. Series of experiments such as X-ray diffraction analysis (XRD), Energy dispersive X-ray diffraction (EDAX), Spectroscopic Ellipsometer, Hall test experiments are carried out to characterize both the bulk and thin films. EDAX experiments show the deviation between bulk and thin films compositions is less than 10%. Diffraction patterns of bulk exhibit orthorhombic structure, i.e., Sb2Se3 type where as thin films demonstrate amorphous behavior. Impact of annealing on thin films is studied by heating the films to 170°C under argon (Ar) ambience. Post annealing results of Sb40Se60 thin films show the crystal structure is orthorhombic and crystallization temperature (Tc) increases with increase in Sb content of the compound. Ellipsometry and Hall measurements of annealed films exhibit high refractive index (n), low extinction coefficient (k) and high carrier concentration with associated low carrier mobility. Further the conductivity of annealed Sb40Se60 thin films switches from p to n type

Doped Metal Oxide High-K Gate Dielectric for Nonvolatile Memory and Light Emitting Applications

The zirconium-doped hafnium oxide (ZrHfO) high-k thin film has excellent gate dielectric properties, such as a higher crystallization temperature, a lower defect density, and a larger effective k value. As a promising high-k material, ZrHfO has been utilized for both nonvolatile memory (NVM) and light emitting applications. Replacing the polycrystalline Si floating gate, the discrete nanocrystals embedded ZrHfO gate dielectric can achieve promising NVM performance. On the other hand, warm white light can be emitted from the thermal excitation of nano-resistors form from the dielectric breakdown of the ZrHfO Metal-Oxide-Semiconductor (MOS) capacitor. This novel solid state incandescent light emitting device (SSI-LED) unveils a new concept for the lighting device evolution. Nanocrystalline cadmium sulfide (nc-CdS) embedded ZrHfO high-k NVMs have been fabricated to reduce the frequency dispersion problem caused by defects at the nanocrystal/dielectric interface. The nc-CdS embedded device can retain about 53% of originally trapped holes for 10 years and exhibit outstanding memory function at low operation voltage. The study on the nc-CdSe embedded ZrHfO NVMs shows that the high temperature enhances the hole trapping but decreases the electron trapping. Based on the different temperature dependences, the stored electrons release faster than stored holes. The raised temperature accelerates the dielectric breakdown process by increasing defect densities and defect effective conduction radii. The post deposition annealing (PDA) atmosphere is critical to the electrical and light emission characteristics of ZrHfO SSI-LEDs. It affects the dielectric breakdown, light emission intensity and efficiency by changing compositions of the high-k stack and the nano-resistor. The electrical properties, i.e., effective resistances and Schottky barrier heights of nano-resistors have been estimated. The nano-resistor behaves neither like a conductor nor like a semiconductor. Moreover, the barrier height inhomogeneity is observed due to the random and complicated nano-resistor formation. The embedding method and the heavily doped p-Si substrate have been employed to enhance the light emission from ZrHfO SSI-LEDs. Lastly, extensive applications of this novel nano-resistor device for on-chip optical interconnects and as diode-like anti-fuses have been discussed